To ensure consistency in the data generated for this study, particularly that of microbiota profiles which are highly variable in phylogenetic composition across individuals, we will centralize all bacterial community profiling pertormed for this Program Project Grant application in Core D. To further standardize microbiome profiling efforts, analyses will be performed using the recently developed G3 16S rRNA PhyloChip, a phylogenetic microarray capable of detecting -60,000 bacterial taxa (defined as groups of organisms that share at least 99% 16S rRNA sequence homology) in a single assay. This approach provides a highresolution profile of the microbiota present, which due to the semi-quantitative data generated, is ideal for both comparative analyses across treatment groups and correlative analyses using measured immunological or environmental variables. The Core will be responsible for extraction of nucleic acids from house dust and stool samples using methods optimized for efficient extraction of both gram positive and negative organisms and for profiling the microbiota present in these samples. Personnel in the Core have extensive expertise in using this technology and in performing microbial ecology studies;these individuals will calculate gross bacterial community composition (BCC) indices (richness, Pielou's evenness and Inverse Simpson's diversity). In addition, Core personnel will generate datasets containing taxonomic relative abundance (based on normalized fluorescence intensity reported by the array) for each sample analyzed. All of this data will be transferred to the Biostatistics Core (Core B) for subsequent large-scale analyses. Use of Core D specifically for microbiome profiling for all Projects in this application ensures standardization of the approach used for microbiome profiling. It also permits data reduction by individuals with extensive expertise in microbiome data analyses using an established analysis pipeline. Finally, this level of consistency in microbiota profiling across all studies proposed in this application will permit cross comparisons between individual studies which is key to ensuring synergism between projects.
Finding discrete differences in microbiota profiles across exposure groups or in association with specific measured variables necessitates a highly standardized approach. The methods proposed in Core D will provide standardized sample handling and processing for bacterial community analysis in environmental and baby stool and, using an economical assay, determine the relative abundance of -60,000 bacterial groups in a single assay, providing a high-resolution profile of community composition for every sample analyzed in this study.
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| Wegienka, Ganesa; Sitarik, Alexandra; Bassirpour, Gillian et al. (2018) The associations between eczema and food and inhalant allergen-specific IgE vary between black and white children. J Allergy Clin Immunol Pract 6:292-294.e2 |
| Sitarik, Alexandra Rene; Kasmikha, Nena Sabri; Kim, Haejin et al. (2018) Breast-feeding and delivery mode modify the association between maternal atopy and childhood allergic outcomes. J Allergy Clin Immunol 142:2002-2004.e2 |
| Cassidy-Bushrow, A E; Burmeister, C; Havstad, S et al. (2018) Prenatal antimicrobial use and early-childhood body mass index. Int J Obes (Lond) 42:1-7 |
| Sitarik, A R; Havstad, S; Levin, A M et al. (2018) Dog introduction alters the home dust microbiota. Indoor Air 28:539-547 |
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| Fonseca, W; Lucey, K; Jang, S et al. (2017) Lactobacillus johnsonii supplementation attenuates respiratory viral infection via metabolic reprogramming and immune cell modulation. Mucosal Immunol 10:1569-1580 |
| Cassidy-Bushrow, Andrea E; Sitarik, Alexandra R; Havstad, Suzanne et al. (2017) Burden of higher lead exposure in African-Americans starts in utero and persists into childhood. Environ Int 108:221-227 |
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